20251118 meeting

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393

import logging
import os
from typing import Any, Dict, Optional, Union, Tuple

import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from torch.utils.checkpoint import checkpoint  # New
from tqdm import tqdm
import numpy as np  # New
import csv  # New

from sssd.core.model_specs import MASK_FN
from sssd.training.utils import training_loss
from sssd.data.utils import get_dataloader
from sssd.utils.logger import setup_logger
from sssd.utils.utils import find_max_epoch, std_normal  # New
from autoFRK import AutoFRK, to_tensor, garbage_cleaner  # New
from autoFRK.utils.helper import cbrt  # New

LOGGER = setup_logger()


class DiffusionTrainer:
    """
    Train Diffusion Models

    Args:
        dataloader (DataLoader): The training dataloader.
        diffusion_hyperparams (Dict[str, Any]): Hyperparameters for the diffusion process.
        net (nn.Module): The neural network model to be trained.
        device (torch.device): The device to be used for training.
        output_directory (str): Directory to save model checkpoints.
        ckpt_iter (Optional[int, str]): The checkpoint iteration to be loaded; 'max' selects the maximum iteration.
        n_iters (int): Number of iterations to train.
        iters_per_ckpt (int): Number of iterations to save checkpoint.
        iters_per_logging (int): Number of iterations to save training log and compute validation loss.
        learning_rate (float): Learning rate for training.
        only_generate_missing (int): Option to generate missing portions of the signal only.
        masking (str): Type of masking strategy: 'mnr' for Missing Not at Random, 'bm' for Blackout Missing, 'rm' for Random Missing.
        missing_k (int): K missing time steps for each feature across the sample length.
        batch_size (int): Size of each training batch.
        logger (Optional[logging.Logger]): Logger object for logging, defaults to None.
    """

    def __init__(
        self,
        data_path: str,
        diffusion_hyperparams: Dict[str, Any],
        net: nn.Module,
        device: Optional[Union[torch.device, str]],
        output_directory: str,
        ckpt_iter: Union[str, int],
        n_iters: int,
        iters_per_ckpt: int,
        iters_per_logging: int,
        learning_rate: float,
        only_generate_missing: int,
        masking: str,
        missing_k: int,
        batch_size: int,
        enable_spatial_prediction: bool,  # New
        autoFRK_period: int,  # New
        location_path: str,  # New
        AFRK_method: str,  # New
        AFRK_tps_method: str,  # New
        logger: Optional[logging.Logger] = None,
    ) -> None:
        loader, ts_mean, ts_std = get_dataloader(
            path=data_path,
            batch_size=batch_size,
            device=device,
        )
        self.dataloader = loader
        self.ts_mean = ts_mean
        self.ts_std = ts_std
        self.diffusion_hyperparams = diffusion_hyperparams
        self.net = nn.DataParallel(net).to(device)
        self.device = device
        self.output_directory = output_directory
        self.ckpt_iter = ckpt_iter
        self.n_iters = n_iters
        self.iters_per_ckpt = iters_per_ckpt
        self.iters_per_logging = iters_per_logging
        self.learning_rate = learning_rate
        self.only_generate_missing = only_generate_missing
        self.masking = masking
        self.missing_k = missing_k
        self.writer = SummaryWriter(f"{output_directory}/log")
        self.batch_size = batch_size
        self.optimizer = torch.optim.Adam(self.net.parameters(), lr=self.learning_rate)
        self.enable_spatial_prediction = enable_spatial_prediction  # New
        self.autoFRK_period = autoFRK_period  # New
        self.location_path = location_path  # New
        self.real_data = self.dataloader.dataset.tensors[0].to(self.device) # * self.ts_std + self.ts_mean # New
        self.real_data_shape = self.real_data.shape  # New
        self.logger = logger or LOGGER
        self.AFRK_mrts = None  # New
        self.AFRK_method = AFRK_method  # New
        self.AFRK_tps_method = AFRK_tps_method  # New
        self.loc = to_tensor(np.load(self.location_path), dtype=self.real_data.dtype, device=device)

        frk = AutoFRK(
            logger_level=30,
            dtype=self.real_data.dtype,
            device=self.real_data.device,
        )
  
        # autoFRK
        def frk_step(data_slice, mrts):
            _ = frk.forward(
                data=data_slice,
                loc=self.loc,
                G=mrts,
                method=self.AFRK_method,
                tps_method=self.AFRK_tps_method,
                requires_grad=True
            )
            pred = frk.predict()['pred.value']
            return pred.T, frk.obj['G']
        #self.frk_step = torch.compile(frk_step)
        self.frk_step = frk_step

        if self.masking not in MASK_FN:
            raise KeyError(f"Please enter a correct masking, but got {self.masking}")

    def _load_checkpoint(self) -> None:
        if self.ckpt_iter == "max":
            self.ckpt_iter = find_max_epoch(self.output_directory)
        if self.ckpt_iter >= 0:
            try:
                model_path = os.path.join(
                    self.output_directory, f"{self.ckpt_iter}.pkl"
                )
                checkpoint = torch.load(model_path, map_location="cpu")

                self.net.load_state_dict(checkpoint["model_state_dict"])
                if "optimizer_state_dict" in checkpoint:
                    self.optimizer.load_state_dict(checkpoint["optimizer_state_dict"])

                self.logger.info(
                    f"Successfully loaded model at iteration {self.ckpt_iter}"
                )
            except Exception as e:
                self.ckpt_iter = -1
                self.logger.error(f"No valid checkpoint model found. Error: {e}")
        else:
            self.ckpt_iter = -1
            self.logger.info(
                "No valid checkpoint model found, start training from initialization."
            )

    def _save_model(self, n_iter: int) -> None:
        if n_iter > 0 and n_iter % self.iters_per_ckpt == 0:
            torch.save(
                {
                    "model_state_dict": self.net.state_dict(),
                    "optimizer_state_dict": self.optimizer.state_dict(),
                },
                os.path.join(self.output_directory, f"{n_iter}.pkl"),
            )

    def _update_mask(self, batch: torch.Tensor) -> torch.Tensor:
        transposed_mask = MASK_FN[self.masking](batch[0], self.missing_k)
        return (
            transposed_mask.permute(1, 0)
            .repeat(batch.size()[0], 1, 1)
            .to(self.device, dtype=torch.float32)
        )
  
    def _sampling_differentiable(
        self,
        net: torch.nn.Module,
        size: Tuple[int, int, int],
        diffusion_hyperparams: Dict[str, torch.Tensor],
        cond: torch.Tensor,
        mask: torch.Tensor,
        only_generate_missing: int = 0,
        device: Union[torch.device, str] = "cpu",
        sampling_mode: str = "ddpm",  # ✅ "ddpm" 或 "ddim"
        step_skip: Union[int, None] = None  # ✅ 若為 None，則自動偵測
    ) -> torch.Tensor:
        _dh = diffusion_hyperparams
        T, Alpha, Alpha_bar, Sigma = _dh["T"], _dh["Alpha"], _dh["Alpha_bar"], _dh["Sigma"]
        x = torch.randn(size, device=device)
        mask = mask.to(device)
        cond = cond.to(device)

        # ✅ 自動偵測最適 step_skip
        if step_skip is None:
            if T <= 200:
                step_skip = 1       # 小模型：全步精細推論
            elif T <= 1000:
                step_skip = max(1, T // 200)  # 一般模型：約 200 步內完成
            else:
                step_skip = max(1, T // 400)  # 大型模型：控制在 400 步以內
            #LOGGER.info(f"T={T}, using step_skip={step_skip}")
        step_skip = max(1, step_skip)

        # ✅ 建立跳步時間表
        timesteps = list(range(T - 1, -1, -step_skip))
        if timesteps[-1] != 0:
            timesteps.append(0)

        noise_list = []
        if sampling_mode == "ddpm":
            for _ in timesteps:
                # pre-generate noise with same shape as x; deterministic for forward+recompute
                noise_list.append(torch.randn(size, device=device))
        else:
            # keep placeholder list (not used for ddim)
            noise_list = [None] * len(timesteps)

        for idx, t in enumerate(timesteps):
            noise_t = noise_list[idx]
            def net_step(x_inner, noise=noise_t, tt=t):
                if only_generate_missing == 1:
                    x_inner = x_inner * (1 - mask).float() + cond * mask.float()
                diffusion_steps = (tt * torch.ones((size[0], 1), device=device))
                epsilon_theta = net((x_inner, cond, mask, diffusion_steps))

                # ✅ 根據 sampling_mode 決定取樣公式
                if sampling_mode == "ddpm":
                    # DDPM：隨機性版本
                    x_next = (x_inner - (1 - Alpha[tt]) / torch.sqrt(1 - Alpha_bar[tt]) * epsilon_theta) / torch.sqrt(Alpha[tt])
                    if tt > 0:
                        x_next = x_next + Sigma[tt] * noise
                elif sampling_mode == "ddim":
                    # DDIM：確定性版本，支援跳步
                    if tt == 0:
                        x_next = torch.sqrt(Alpha_bar[tt]) * (
                            (x_inner - torch.sqrt(1 - Alpha_bar[tt]) * epsilon_theta)
                            / torch.sqrt(Alpha_bar[tt])
                        )
                    else:
                        Alpha_bar_prev = Alpha_bar[max(tt - step_skip, 0)]
                        x0_pred = (x_inner - torch.sqrt(1 - Alpha_bar[tt]) * epsilon_theta) / torch.sqrt(Alpha_bar[tt])
                        x_next = torch.sqrt(Alpha_bar_prev) * x0_pred + torch.sqrt(1 - Alpha_bar_prev) * epsilon_theta
                else:
                    raise ValueError(f"Unknown sampling_mode: {sampling_mode}")

                return x_next
            x = checkpoint(net_step, x, use_reentrant=False)
            #x = net_step(x)

        return x
  
    def _sssd_prediction_step(self) -> torch.Tensor:
        self.logger.info(f"Start SSSD prediction step")

        all_batches = []
        for (batch,) in tqdm(self.dataloader, desc=f"{self.n_iter}-th predicting TS"):
            mask = self._update_mask(batch)
            batch = batch.permute(0, 2, 1)

            batch_generated = self._sampling_differentiable(
                net=self.net,
                size=batch.shape,
                diffusion_hyperparams=self.diffusion_hyperparams,
                cond=batch,
                mask=mask,
                only_generate_missing=self.only_generate_missing,
                device=self.device,
                sampling_mode=getattr(self, "sampling_mode", "ddim"),
                step_skip=getattr(self, "step_skip", 10)
            )
            all_batches.append(batch_generated)

        sssd_prediction = torch.cat(all_batches, dim=0).permute(1, 2, 0)

        return sssd_prediction

    def _autoFRK_step(self,
                      sssd_prediction,
                      ) -> torch.Tensor:
        # autoFRK
        self.logger.info(f"Start autoFRK inference step")
        dtype = sssd_prediction.dtype
        device = sssd_prediction.device
  
        V, T, N = sssd_prediction.shape
        autoFRK_result = torch.empty_like(sssd_prediction, dtype=dtype, device=device)
        frk_step = self.frk_step

        # mrts
        if self.AFRK_mrts is None:
            data_slice = sssd_prediction[0, :, :].T
            _, mrts = frk_step(data_slice, None)
            self.AFRK_mrts = mrts
        else:
            mrts = self.AFRK_mrts

        # Flatten 迴圈
        for idx in tqdm(range(V), desc=f"{self.n_iter}-th predicting autoFRK"):
            data_slice = sssd_prediction[idx, :, :].T
            try:
                pred, _ = frk_step(data_slice, mrts)
            except Exception as e:
                self.logger.warning(f"Variable {idx} numerical issue: {e}; using fallback clone")
                pred = data_slice.clone()
            autoFRK_result[idx, :, :] = pred
        nan_idx = torch.isnan(autoFRK_result)
        if nan_idx.any():
            self.logger.info(f"Replacing total {nan_idx.sum().item()} NaNs in autoFRK_result with original sssd_prediction values")
            autoFRK_result = torch.where(nan_idx, sssd_prediction, autoFRK_result)
        autoFRK_result = autoFRK_result.permute(2, 1, 0)

        # return
        if autoFRK_result.shape != self.real_data_shape:
            error_msg = f"Shape mismatch: autoFRK_result {autoFRK_result.shape} != real_data {self.real_data_shape}"
            self.logger.error(error_msg)
            raise ValueError(error_msg)

        return autoFRK_result

    def _train_per_epoch(self) -> torch.Tensor:
        loss_function=nn.MSELoss()
        #diffusion_loss = torch.tensor(0.0, device=self.device, dtype=torch.float32)
        #n_batches = 0

        # SSSD training
        for batch_idx, (batch,) in enumerate(tqdm(self.dataloader, desc=f"{self.n_iter}-th training TS")):
            batch = batch.to(self.device)

            # New
            mask = self._update_mask(batch)
            loss_mask = ~mask.bool()
            self.optimizer.zero_grad()

            if loss_mask.sum() == 0:
                self.logger.warning(f"Batch {batch_idx} has no valid elements for loss")
                continue

            batch = batch.permute(0, 2, 1)
            assert batch.size() == mask.size() == loss_mask.size()
  
            loss = training_loss(
                model=self.net,
                loss_function=loss_function,
                training_data=(batch, batch, mask, loss_mask),
                diffusion_parameters=self.diffusion_hyperparams,
                generate_only_missing=self.only_generate_missing,
                device=self.device,
            )

            if not (self.enable_spatial_prediction and self.n_iter % self.autoFRK_period == 0):
                loss.backward()
                self.optimizer.step()
                continue

            loss.backward(retain_graph=True)
            self.optimizer.step()
            #diffusion_loss = diffusion_loss + loss
            #n_batches += 1

        if self.enable_spatial_prediction and self.n_iter % self.autoFRK_period == 0:
            self.logger.info(f"Iteration {self.n_iter}: Start Spatial Prediction step")
            sssd_prediction = self._sssd_prediction_step()
            autoFRK_result = self._autoFRK_step(sssd_prediction=sssd_prediction)

            # compute loss
            self.optimizer.zero_grad()
            spatial_loss = loss_function(
                autoFRK_result,
                self.real_data
            )
            #loss = spatial_loss + diffusion_loss / n_batches
            loss = spatial_loss

            # update model
            loss.backward()
            self.optimizer.step()
  
        self.logger.info(f"Iteration {self.n_iter}: loss: {loss.item()}")
  
        return loss

    def train(self) -> None:
        self._load_checkpoint()

        n_iter_start = (
            self.ckpt_iter + 2 if self.ckpt_iter == -1 else self.ckpt_iter + 1
        )
        self.logger.info(f"Start the {n_iter_start} iteration")

        for n_iter in range(n_iter_start, self.n_iters + 1):
            self.n_iter = n_iter
            loss = self._train_per_epoch()
            self.writer.add_scalar("Train/Loss", loss.item(), n_iter)
            if n_iter % self.iters_per_logging == 0:
                self.logger.info(f"Iteration: {n_iter} \tLoss: { loss.item()}")
            self._save_model(n_iter)

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239

import logging
import os
from typing import Dict, Iterable, Optional, Union

import numpy as np
import torch
from sklearn.metrics import mean_absolute_percentage_error, mean_squared_error
from torch.utils.data import DataLoader
from tqdm import tqdm  # New

from sssd.core.model_specs import MASK_FN
from sssd.data.utils import get_dataloader  # New
from sssd.utils.logger import setup_logger
from sssd.utils.utils import find_max_epoch, sampling
from autoFRK import AutoFRK, to_tensor, garbage_cleaner  # New

LOGGER = setup_logger()


class DiffusionGenerator:
    """
    Generate data based on ground truth.

    Args:
        net (torch.nn.Module): The neural network model.
        device (Optional[Union[torch.device, str]]): The device to run the model on (e.g., 'cuda' or 'cpu').
        diffusion_hyperparams (dict): Dictionary of diffusion hyperparameters.
        local_path (str): Local path format for the model.
        testing_data (torch.Tensor): Tensor containing testing data.
        output_directory (str): Path to save generated samples.
        batch_size (int): Number of samples to generate.
        ckpt_path (str): Checkpoint directory.
        ckpt_iter (str): Pretrained checkpoint to load; 'max' selects the maximum iteration.
        masking (str): Type of masking: 'mnr' (missing not at random), 'bm' (black-out), 'rm' (random missing).
        missing_k (int): Number of missing time points for each channel across the length.
        only_generate_missing (int): Whether to generate only missing portions of the signal:
                                      - 0 (all sample diffusion),
                                      - 1 (generate missing portions only).
        saved_data_names (Iterable[str], optional): Names of data arrays to save (default is ("imputation", "original", "mask")).
        logger (Optional[logging.Logger], optional): Logger object for logging messages (default is None).
    """

    def __init__(
        self,
        net: torch.nn.Module,
        device: Optional[Union[torch.device, str]],
        diffusion_hyperparams: dict,
        local_path: str,
        data_path: str,
        output_directory: str,
        batch_size: int,
        ckpt_path: str,
        ckpt_iter: str,
        masking: str,
        missing_k: int,
        only_generate_missing: int,
        enable_spatial_prediction: bool,  # New
        known_location_path: str,  # New
        unknown_location_path: str,  # New
        AFRK_method: str,  # New
        AFRK_tps_method: str,  # New
        saved_data_names: Iterable[str] = ("imputation", "original", "mask"),
        logger: Optional[logging.Logger] = None,
    ):
        self.net = net
        self.device = device
        self.diffusion_hyperparams = diffusion_hyperparams
        self.local_path = local_path
        loader, ts_mean, ts_std = get_dataloader(
            path=data_path,
            batch_size=batch_size,
            device=device,
            inference=True,
            missing_k=missing_k
        )
        self.dataloader = loader
        self.ts_mean = ts_mean
        self.ts_std = ts_std
        self.batch_size = batch_size
        self.masking = masking
        self.missing_k = missing_k
        self.only_generate_missing = only_generate_missing
        self.enable_spatial_prediction = enable_spatial_prediction  # New
        self.known_location_path = known_location_path  # New
        self.unknown_location_path = unknown_location_path  # New
        self.missing_k = missing_k  # New
        self.AFRK_method = AFRK_method  # New
        self.AFRK_tps_method = AFRK_tps_method # New
        self.logger = logger or LOGGER

        self.output_directory = self._prepare_output_directory(
            output_directory, local_path, ckpt_iter
        )
        self.saved_data_names = saved_data_names
        self._load_checkpoint(ckpt_path, ckpt_iter)

    def _load_checkpoint(self, ckpt_path: str, ckpt_iter: str) -> None:
        """Load a checkpoint for the given neural network model."""
        ckpt_path = os.path.join(ckpt_path, self.local_path)
        if ckpt_iter == "max":
            ckpt_iter = find_max_epoch(ckpt_path)
        model_path = os.path.join(ckpt_path, f"{ckpt_iter}.pkl")
        try:
            checkpoint = torch.load(model_path, map_location="cpu")
            self.net.load_state_dict(checkpoint["model_state_dict"])
            self.logger.info(f"Successfully loaded model at iteration {ckpt_iter}")
        except FileNotFoundError as e:
            raise FileNotFoundError(f"Model file not found at {model_path}") from e
        except Exception as e:
            raise Exception(f"Failed to load model: {e}")

    def _prepare_output_directory(
        self, output_directory: str, local_path: str, ckpt_iter: str
    ) -> str:
        """Prepare the output directory to save generated samples."""
        ckpt_iter_str = (
            "max"
            if ckpt_iter == "max"
            else f"imputation_multiple_{int(ckpt_iter) // 1000}k"
        )
        output_directory = os.path.join(output_directory, local_path, ckpt_iter_str)
        os.makedirs(output_directory, exist_ok=True)
        os.chmod(output_directory, 0o775)
        self.logger.info(f"Output directory: {output_directory}")
        return output_directory

    def _update_mask(self, batch: torch.Tensor) -> torch.Tensor:
        """Update mask based on the given batch."""
        transposed_mask = MASK_FN[self.masking](batch[0], self.missing_k)
        return (
            transposed_mask.permute(1, 0)
            .repeat(batch.size()[0], 1, 1)
            .to(self.device, dtype=torch.float32)
        )

    def _save_data(
        self,
        results: Dict[str, np.ndarray],
        index: int,
    ) -> None:
        """Save generated_series, batch, and mask data arrays."""

        for name, data in results.items():
            if name in self.saved_data_names:
                filename = f"{name}{index}.npy"
                np.save(os.path.join(self.output_directory, filename), data)

    def _autoFRK_generate(
        self,
        sssd_inference,
        with_known_loc: bool = True
    ) -> torch.Tensor:
        LOGGER.info(f"Start autoFRK inference step")
        dtype = torch.float32
        device = sssd_inference.device
        sssd_inference = to_tensor(sssd_inference, dtype = dtype, device = device)

        N, T, V = sssd_inference.shape

        known_loc = to_tensor(np.load(self.known_location_path), dtype=dtype, device=device)
        unknown_loc = to_tensor(np.load(self.unknown_location_path), dtype=dtype, device=device)
        if known_loc.ndim == 1:
            known_loc = known_loc.view(-1, 1)
        if unknown_loc.ndim == 1:
            unknown_loc = unknown_loc.view(-1, 1)
        missing_loc = unknown_loc.shape[0]
        autoFRK_inference = torch.zeros((missing_loc, T, V), dtype=dtype, device=device)
        print(f"missing_loc : {missing_loc}")
        print(f"autoFRK_inference : {autoFRK_inference.shape}")
        frk = AutoFRK(
            logger_level=30,
            dtype=dtype,
            device=device,
        )

        mrts = None
        for variable in tqdm(range(V), desc=f"inferencing autoFRK"):
            data_slice = sssd_inference[:, :, variable]
            try:
                _ = frk.forward(
                    data=data_slice,
                    loc=known_loc,
                    G = mrts,
                    method=self.AFRK_method,
                    tps_method=self.AFRK_tps_method,
                    requires_grad=False
                )
                pred = frk.predict(
                    newloc = unknown_loc
                )['pred.value']
                mrts = frk.obj['G'] if mrts is None else mrts

            except torch._C._LinAlgError:
                LOGGER.warning(f"Skipped variable={variable} due to ill-conditioned matrix")
                pred = torch.zeros((missing_loc, T), dtype=dtype, device=device)
            
            print(f"pred : {pred.shape}")
            print(f"autoFRK_inference[:, :, variable]  : {autoFRK_inference[:, :, variable] .shape}")
            autoFRK_inference[:, :, variable] = pred

        if with_known_loc:
            autoFRK_inference = torch.cat([sssd_inference, autoFRK_inference], dim=0)

        return autoFRK_inference

    def generate(self) -> list:
        """Generate samples using the given neural network model."""
        all_generated = []
        for index, (batch,) in tqdm(enumerate(self.dataloader), total=len(self.dataloader), desc="inferencing sssd"):
            batch = batch.to(self.device)
            mask = self._update_mask(batch)
            batch = batch.permute(0, 2, 1)

            generated_series = (
                sampling(
                    net=self.net,
                    size=batch.shape,
                    diffusion_hyperparams=self.diffusion_hyperparams,
                    cond=batch,
                    mask=mask,
                    only_generate_missing=self.only_generate_missing,
                    device=self.device,
                )
            )
            all_generated.append(generated_series)
        sssd_inference = torch.cat(all_generated, dim=0).permute(0, 2, 1)
        ts_mean = to_tensor(self.ts_mean, dtype = torch.float32, device = sssd_inference.device)
        ts_std = to_tensor(self.ts_std, dtype = torch.float32, device = sssd_inference.device)
        sssd_inference = sssd_inference * ts_std + ts_mean

        if self.enable_spatial_prediction:
            autoFRK_inference = self._autoFRK_generate(
                sssd_inference = sssd_inference,
                with_known_loc = True
            )
            results = {'imputation': autoFRK_inference.detach().cpu().numpy()}
        else:
            results = {'imputation': sssd_inference.detach().cpu().numpy()}
        self._save_data(results, 0)

1
2
3
4
5
6
7

    if ncol_Fk > 2:
        reduced_columns = torch.cat([
            torch.tensor([0], dtype=torch.int64, device=device),
            (d_hat[1:(ncol_Fk - 1)] > 0).nonzero(as_tuple=True)[0]
        ])
    else:
        reduced_columns = torch.tensor([ncol_Fk - 1], dtype=torch.int64, device=device)

1
2
3
4
5
6
7

    if ncol_Fk > 2:
        reduced_columns = torch.unique(torch.cat([
            torch.tensor([0], dtype=torch.int64, device=device),
            (d_hat[1:(ncol_Fk - 1)] > 0).nonzero(as_tuple=True)[0]
        ]))
    else:
        reduced_columns = torch.tensor([ncol_Fk - 1], dtype=torch.int64, device=device)

1
2
3
4
5

if (ncol_Fk > 2) {
      reduced_columns <- c(1, which(d_hat[2:ncol_Fk] > 0))
    } else {
      reduced_columns <- ncol_Fk
    }

1
2
3
4
5

if (ncol_Fk > 2) {
      reduced_columns <- unique(c(1, which(d_hat[2:ncol_Fk] > 0)))
    } else {
      reduced_columns <- ncol_Fk
    }